System, method, and computer algorithm for measuring, displaying, and accurately detecting changes in electrophysiological evoked potentials

ABSTRACT

An automated evoked potential analysis apparatus for improved monitoring, detecting and identifying changes to a patient&#39;s physiological system is described. The apparatus includes an input device for obtaining electrical potential data from the patient&#39;s physiological system after application of stimulation to a patient&#39;s nerve and a computing system for receiving and analyzing the electrical potential data. The computing system includes a processing circuit configured to: generate a plurality of evoked potential waveforms (EPs) based on the electrical potential data, measure changes or trends in the generated EPs utilizing a sliding window of analysis, display incremental changes in waveforms between full subsets of EPs, and determine an alert vote for each subset, representative of changes to the physiological system generating the EPs.

RELATED APPLICATIONS

This application claims priority from Provisional Application U.S.Application 62/156,874, filed May 4, 2015, entitled “SYSTEM, METHOD, ANDCOMPUTER ALGORITHM FOR MEASURING, DISPLAYING, AND ACCURATELY DETECTINGCHANGES IN ELECTROPHYSIOLOGICAL EVOKED POTENTIALS” incorporated hereinby reference in its entirety. This application is also related to U.S.patent application Ser. No. 13/874,867 (published as U.S. PatentPublication 2014/0020178), filed May 1, 2013, entitled “SYSTEM, METHOD,AND COMPUTER ALGORITHM AND CHARACTERIZATION AND CLASSIFICATION OFELECTROPHYSIOLOGICAL EVOKED POTENTIALS,” which is hereby incorporated byreference in its entirety.

BACKGROUND

The present invention relates generally to computer programs and methodsfor detecting changes in evoked potential (EP) waveforms, and moreparticularly to systems, methods, and computer-readable medium, whichuse a mathematical algorithm to assess and display EP waveforms, andcalculate alerts to waveform changes.

Standard Attended Intraoperative Monitoring

Somatosensory evoked potentials are summated electrical potentialsusually recorded from the head or neck area and a peripheral nerve afterrepeatedly stimulating a peripheral nerve. Monitoring patients usingsomatosensory evoked potentials during surgery has been shown to allowearly identification of impending injury, particularly nerve injury.

Such monitoring generally requires highly trained technologists underphysician supervision with sophisticated, multichannel amplifier anddisplay equipment. Unfortunately, such personnel and equipment arelimited in their availability, require pre-booking, and are costly. Inaddition, such monitoring is fraught with difficulties due to the smallsize of potentials and ongoing noise which make recognizing significantchanges, and alerting of these changes, difficult. In current systemsthat are used to generate alerts automatically, substantial noise andvariability can cause false alerts.

Embodiments described herein relate to improved, systems, methods anddevices for accurately detecting changes in electrophysiological evokedpotentials. Improvements to existing systems include reduction in falsepositive/false negative alerts due to signal noise. Accordingly, theimproved systems, methods, and devices generate more accurate alerts.Reducing the number of false positive alerts also creates more efficientsystems, methods, and devices compared to known systems.

SUMMARY OF THE INVENTION

Embodiments described herein relate to methods, devices, systems,apparatuses, and/or means to automatically detect and display changes tothe evoked potential waveforms (EPs) in real time while showingincremental changes between completed ensemble averages. Embodimentsdescribed herein also relate to producing alerts with reduced oreliminated influence of variable noise and bias, while minimizing oreliminating false negative and false positive errors. The presentdisclosure generally relates to the computer signal processing anddisplay algorithms for the characterization and classification ofchanges to EPs in real-time. The disclosed embodiments can be used inlieu of expert analysis typically provided by the technologist andphysician. Further, various embodiments can be used in conjunction withother equipment. For example, upon detecting a change in EPs, anoperating room table can be moved or adjusted. Such movement allows thepatient to be automatically moved to ameliorate or avoid patient injury.Thus various embodiments herein extend the benefit of such equipment byautomatically controlling the equipment based upon the EPs.

When seeking to accurately monitor or detect somatosensory evokedpotentials (SSEPs), the waveforms can require acquisition at specificfrequencies and/or averaging together to help eliminate random andcyclical noise. Even a few aberrant waveforms heavily affected by noisecan markedly change the apparent amplitude (height) or latency (time ofonset) of a waveform of interest when averaged together. While this ispartly avoided by careful choice of stimulation frequencies andfiltering of the waveforms, such methods cannot be complete as thewaveforms of interest fall within the frequency range of the backgroundnoise and the cyclical background noise varies somewhat in frequency. Inaddition, these methods require producing a complete average (EnsembleAverage or EA) of 100-300 stimulations and resultant waveforms, and thenconfirming any suspected change based on comparison with another EA,each taking up to 3 minutes to collect.

There is presently no way of observing slow onset incremental changes ordiscounting aberrant epochs affected by noise. The cost of havingprofessionals fully engaged in interpreting these waveforms results inlimiting of the service to only the most high risk surgeries. Inaddition, interpretation may be biased by human factors such as priornegative patient outcomes.

Thus, embodiments herein generally relate to systems, methods, devicesand computer algorithms for measuring, displaying, and accuratelydetecting changes in electrophysiological evoked potentials. In someaspects the embodiments can automatically detect and display changes tothe EPs in real time, including while showing incremental changesbetween completed ensemble averages. Also, in some aspects theembodiments can provide alerts free of the influence of variable noiseand bias, including while minimizing or eliminating false negative andfalse positive errors. Embodiments described herein generally relate tothe computer signal processing and display algorithms for thecharacterization and classification of changes to EPs in real-timeimplemented on specialized devices and systems. This systems and devicesutilizing the algorithms may substitute for the expert analysistypically provided by the technologist and physician.

In an exemplary embodiment of the present invention a system, method,and computer algorithm for measuring, displaying and accuratelydetecting changes in electrophysiological evoked potentials isdisclosed. In this disclosure, an EP is defined as a voltage versus timesignal obtained by ensemble averaging of the electrophysiologicalresponses to repetitive stimulation of a specific neural system,detected using suitable electrodes. Examples of EPs are somatosensory,auditory or visual EPs. The algorithms are applied to a time sequence ofEPs acquired over the course of an ongoing clinical procedure. Thealgorithms establish changes to the characteristics of an EP relative tothe baseline/normal EP, as well as to any previous EPs to determine ifthe functioning of the underlying neural system has been significantlyaffected by the ongoing clinical procedure. The algorithms communicatewith ancillary hardware and algorithms developed to acquire the sequenceof EPs and provide suitable feedback to ensure a safe and effectiveclinical workflow. The algorithms provide the basis for a clinicallyeffective application such that false positives and false negatives areminimized.

Further features and advantages of the invention, as well as thestructure and operation of various embodiments of the invention, aredescribed in detail below with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the invention will beapparent from the following, more particular description of a preferredembodiment of the invention, as illustrated in the accompanyingdrawings.

FIG. 1 illustrates the traditional method evoked potential waveform (EP)acquisition and display.

FIG. 2 illustrates a sliding window method of EP acquisition, display,and alerting, according to an exemplary embodiment of the presentinvention.

FIG. 3 is a block diagram of a model EP analysis apparatus according toan exemplary embodiment.

DETAILED DESCRIPTION

Various exemplary embodiments of the invention including preferredembodiments are discussed in detail below. While specific exemplaryembodiments are discussed, it should be understood that this is done forillustration purposes only. A person skilled in the relevant art willrecognize that other components and configurations can be used withoutparting from the spirit and scope of the invention.

FIG. 1. illustrates a traditional way in which EPs are acquired anddisplayed. Using this method, a series of stimulations are applied andthe resultant individual waveforms are displayed either as each isacquired, or as an evolving average of the ongoing average until a finalensemble average is acquired and displayed. The clinical interpretationis based on this ensemble average. If the user is satisfied with theaverage they may set that average as the ‘baseline’ to which all othersare compared, or repeat it to confirm the presence and morphology of thewaveform of interest. The whole process is then repeated with the nextseries of stimulations. Comparison of that series is then made to thepreceding one that is chosen to be the baseline.

Since as few as five or six noisy individual waveforms that escapefiltering can cause the waveform morphology, amplitude, and latency towidely vary, applying this method in an electrically noisy environmentmay lead to erroneous alerting of potential, imminent injury if anautomated alerting system is used. This generally requires expertinterpretation of the individual waveforms that takes into account theclinical situation, expected waveform and general trend in the waveformpattern over time.

An embodiment of the present invention relates to the computer signalprocessing and algorithms for the display of EP waveforms, calculationof alerts to waveform changes, and minimization of false positive andfalse negative alerts. This algorithm may substitute for the expertanalysis typically provided by the technologist and physician. Thecomputer algorithm running in software installed on an EP machine may beused in any surgery or situation where a patient is at risk, in order todetect, alert, and ameliorate positioning effect, or any nerve injury orabnormality.

FIG. 2. illustrates an exemplary depiction of a sliding window ofanalysis and alerting using metadata from automatically calculatedalerts that takes into consideration variation in noise andautomatically looks back at the trend of the waveform over time,according to the present invention.

After collection of one or more sets of EPs, an Ensemble Average (EA) iscreated and a baseline of the waveform of interest is calculated. Thebaseline waveform may be identified by any standard technique includingfor example wavelet analysis, curve fitting etc.

For the next “n” number of stimulations, a new EA is created usingsimilar waveforms as the previous one but from which a number of EPs areremoved from the beginning of the average and a similar number is addedto the end. For example, as shown on FIG. 2, EP2 does not incorporatethe first two EPs of EP1, and includes two additional EPs at the end.Thus achieving a “sliding window” selection of EP data on which tocreate the new EA for the ongoing stimulation series. In certainpreferred embodiments, a new EA is created for every epoch, i.e. the“window” slides forward only one EP, eliminating the first and adding anadditional one to the end, relative to the previous EP selection for theprevious EA.

The overall average can be updated and displayed with every overlappingepoch allowing a slow change in the morphology to evolve for the viewer.In this way, the viewer observes a real-time, incremented change in theEP waveform morphology over time, similar to viewing a live event ormotion picture. In an exemplary embodiment of the invention, thegenerated overlapping EAs allows display of the effect of incrementalconsequence on the baseline average caused by newly collected waveformsin between non-overlapping EAs.

Each new EA waveform is identified and the salient characteristic (forexample amplitude or latency) is measured and recorded. The new valuesare compared to the baseline value of the characteristic and it isdetermined whether an alert vote is to be generated. An alert vote isgenerated if the characteristic reaches a change threshold, i.e., adegree of change the user has set to trigger an alert. This process isrepeated. Each new EA generates a vote as to whether an alert should betriggered or not. When the number of EAs reaches a specific number (n),typically when the initial stimuli of the EA no longer overlap thebaseline EA, the votes are tallied and, in preferred embodiments, onlythen can an alert be triggered depending upon the ratio of yes to no(Y/N) alert votes required to trigger an alert. The user may alter theratio of alert votes (Y/N) required depending upon their wish forspecificity of the alerting process. In other embodiments, the votes maybe continuously tallied and an alert triggered once the yes (Y) votesreach a certain predetermined number, or the ratio reaches apredetermined value.

In an exemplary embodiment of the invention, since the voting processmay be asymmetrical, different ratios of voting may be used fortriggering (onset) and releasing (offset) a final alert to the user thatnerve injury to the patient is possible or imminent.

The alerting process examines the meta-data (the alerting vote) for theindividual overlapping epochs. Since any real change in data will besustained and always eventually deliver 100% (or close) Y/N votes foralert, the specificity of the alerting process can now be manipulatedindependent of the sensitivity of the system on the basis of the voting.For example, the user may alter the ratio to be less specific, requiringonly 30% or 50% of votes vs 80% of votes for more specificity. In thisway, there can be a reduction in the number of false alerts withoutreally altering the sensitivity of the process to detecting realsustained change. In currently used systems, substantial noise andvariability can cause false alerts, whereas the voting ratio usedaccording to the present invention lessens the impact of the noisysignals. In an exemplary embodiment of the invention, EAs with wildlyvariant waveform values can be discarded from the voting process,eliminating intermittent noise that escapes the frequency filters. Thus,embodiments have an improved way of handling the effects of noise inEPs. The effect of this noise is further nullified by the voting processitself.

Going forward the process is repeated, the voting using the last ‘n’number of EAs to decide if an alert is triggered and displaying each EAor an averaged group of EAs as a progressively changing waveform whichcan easily be compared to baseline visually. In various embodiments,when an alert is triggered other actions can also occur. For example,information leading to the alert trigger can be sent to other equipment.As another example, a command to move a portion of an operating roomtable can be sent to the operating room table. Upon receipt, theoperating room table can move and thus move the position of the patient.The movement reduces the patient's risk of nerve injury.

In an exemplary embodiment of the invention, once the process isestablished, a smooth ongoing generation of overlapping EAs can identifygradual changes that are due to processes such as cooling or depth ofanesthesia that are not due to more abrupt onset injuries.

Referring to FIG. 3, according to an exemplary embodiment, the EPanalysis described above is implemented with an EP analysis apparatus30. The EP analysis apparatus 30 includes hardware and software foroperation and control of the system. According to an exemplaryembodiment, the EP analysis apparatus 30 includes a computing system 31,an input device 36, and a display device 37. The computing systemcomprises a processing circuit 32 having a processor 33 and memory 34.Processor 33 can be implemented as a general purpose processor, anapplication specific integrated circuit (ASIC), one or more fieldprogrammable gate arrays (FPGAs), a group of processing components, orother suitable electronic processing components. Memory 34 (e.g.,memory, memory unit, storage device, etc.) is one or more devices (e.g.,RAM, ROM, Flash-memory, hard disk storage, etc.) for storing data and/orcomputer code for completing or facilitating the various processesdescribed in the present application. Memory 34 may be or includevolatile memory or non-volatile memory. Memory 34 may include databasecomponents, object code components, script components, or any other typeof information structure for supporting the various activities describedin the present application. According to an exemplary embodiment, memory34 is communicably connected to processor 33 and includes computer codefor executing one or more processes described herein. The memory 34 maycontain a variety of modules, each capable of storing data and/orcomputer code related to specific types of functions.

Referring still to FIG. 3, the computing system 31 further includes acommunication interface 35. The communication interface 35 can be orinclude wired or wireless interfaces (e.g., jacks, antennas,transmitters, receivers, transceivers, wire terminals, etc.) forconducting data communications with external sources via a directconnection or a network connection (e.g., an Internet connection, a LAN,WAN, or WLAN connection, etc.).

What is claimed is:
 1. An automated evoked potential analysis apparatusfor monitoring, detecting and identifying changes to a patient'sphysiological system, wherein the apparatus comprises: an input devicefor obtaining electrical potential data from the patient's physiologicalsystem after application of stimulation to a nerve of the patient; acomputing system for receiving and analyzing the electrical potentialdata, and comprising a processing circuit configured to: generate aplurality of evoked potential waveforms (EPs) based on the electricalpotential data; calculate a first ensemble average waveform (EA) of afirst subset of the plurality of EPs; display the first EA; calculate asecond EA of a second subset of the plurality of EPs, wherein the secondsubset eliminates at least one initial EP from the first subset of EPsand includes at least one additional EP from after the termination ofthe first subset, and wherein the first and second subsets comprise thesame number of EPs and represent a sliding window for generating aplurality of EAs for corresponding subsets of the plurality of EPs, theplurality of EAs comprising the first EA and the second EA; display thesecond EA to display incremental changes between the first and secondsubsets of EPs; identify a characteristic in each EA, the characteristiccomprising a latency, an amplitude, or a morphology; determine an alertvote for each subset, representative of changes to the physiologicalsystem generating the EPs, the determining the alert vote comprising:creating a positive alert vote if the characteristic of a particular EAreaches a change threshold compared to the characteristic of a precedingEA; and creating a negative alert vote if the characteristic of theparticular EA does not reach the change threshold compared to thepreceding EA; calculate, after calculating the first EA and the secondEA, a third EA of a third subset of the plurality of EPs, wherein thethird subset and the first subset do not overlap; determine, aftercalculating the third EA of the third subset of the plurality of EPs,whether a ratio of positive alert votes to negative alert votes exceedsa threshold value; and trigger, upon determining that the ratio exceedsthe threshold value, an alert.
 2. The apparatus of claim 1, wherein saidapparatus is further configured to integrate into other devices in asurgical environment.
 3. The apparatus of claim 2, wherein saidcomputing system is further configured to feed information to otherdevices in the surgical environment that allows these devices tomanually or automatically ameliorate or mitigate the physiologicalchanges and improve subsequently acquired EP waveforms.
 4. The apparatusof claim 3, further comprising an operating table, wherein the operatingtable is configured to be adjustable to ameliorate or mitigate thephysiological changes by causing an adjustment of the patient'sposition, in response to the information from the computing system. 5.The apparatus of claim 1, wherein said apparatus computing system isfurther configured to: obtain information from an anesthesia or bloodpressure machine; and determine when changes in EP waveforms are due toanesthesia or blood pressure changes.
 6. A method of automaticallyidentifying potential injury to peripheral nerve structures, the methodcomprising: stimulating, by an output device of an automated evokedpotential analysis apparatus, a peripheral nerve of a patient withelectrical pulses; recording, by an input device of the automated evokedpotential analysis apparatus, resultant electrical waveforms (EPs)generated by the physiological system of the patient, wherein the inputdevice comprises electrodes placed over a nerve pathway; measuring, by acomputing system of the automated evoked potential analysis apparatus,changes in the acquired EPs utilizing a sliding window of analysis, thecomputing system comprising a processing circuit, wherein utilizing thesliding window of analysis comprises: calculating, by the computingsystem, a first ensemble average waveform (EA) of a first subset of theplurality of EPs, displaying, by the computing system, the first EA,calculating, by the computing system, a second EA of a second subset ofthe plurality of EPs, wherein the second subset eliminates at least oneinitial EP from the first subset of EPs and includes at least oneadditional EP from after the termination of the first subset, andwherein the first and second subsets comprise the same number of EPs andrepresent a sliding window for generating a plurality of EAs forcorresponding subsets of the plurality of EPs, the plurality of EAscomprising the first EA and the second EA, and displaying, by thecomputing system, the second EA to display incremental changes betweenthe first and second subsets of EPs; and identifying, by the computingsystem, a characteristic in each EA, the characteristic comprising alatency, an amplitude, or a morphology; determining, by the computingsystem, an alert vote for each subset, representative of changes to thephysiological system generating the EPs, the determining the alert votecomprising: creating, by the computing system, a positive alert vote ifthe characteristic of a particular EA reaches a change thresholdcompared to the characteristic of a preceding EA; and creating, by thecomputing system, a negative alert vote if the characteristic of theparticular EA does not reach the change threshold compared to thepreceding EA; calculating, by the computing system after calculating thefirst EA and the second EA, a third EA of a third subset of theplurality of EPs, wherein the third subset and the first subset do notoverlap; determining, by the computing system and after calculating thethird EA of the third subset of the plurality of EPs, whether a ratio ofpositive alert votes to negative alert votes exceeds a threshold value;triggering, by the computing system, upon determining that the ratioexceeds the threshold value, an alert to the user; and alerting, by thecomputing system, the user to the changes.
 7. The method of claim 6,further comprising passing, by the computing system, information relatedto alerts to an automated operating room table and readjusting thepatient's position through adjustment of the table.
 8. The method ofclaim 7, wherein adjustment of the operating room table is performed, bythe computing system, automatically or semi automatically.
 9. The methodof claim 6, further comprising passing, by the computing system,information related to alerts to other devices or processes.
 10. Themethod of claim 6, wherein the electrodes are placed at the neck or headof the patient.